Overview

Dataset statistics

Number of variables23
Number of observations3677
Missing cells6710
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory667.8 B

Variable types

Categorical13
Numeric10

Alerts

society has a high cardinality: 676 distinct valuesHigh cardinality
sector has a high cardinality: 113 distinct valuesHigh cardinality
areaWithType has a high cardinality: 2355 distinct valuesHigh cardinality
price is highly overall correlated with price_per_sqft and 6 other fieldsHigh correlation
price_per_sqft is highly overall correlated with priceHigh correlation
area is highly overall correlated with price and 4 other fieldsHigh correlation
bedRoom is highly overall correlated with price and 5 other fieldsHigh correlation
bathroom is highly overall correlated with price and 5 other fieldsHigh correlation
super_built_up_area is highly overall correlated with price and 7 other fieldsHigh correlation
built_up_area is highly overall correlated with super_built_up_area and 2 other fieldsHigh correlation
carpet_area is highly overall correlated with price and 5 other fieldsHigh correlation
property_type is highly overall correlated with price and 2 other fieldsHigh correlation
facing is highly overall correlated with built_up_areaHigh correlation
servant room is highly overall correlated with bathroom and 1 other fieldsHigh correlation
store room is highly imbalanced (55.7%)Imbalance
facing has 1045 (28.4%) missing valuesMissing
super_built_up_area has 1802 (49.0%) missing valuesMissing
built_up_area has 1987 (54.0%) missing valuesMissing
carpet_area has 1805 (49.1%) missing valuesMissing
area is highly skewed (γ1 = 29.73095613)Skewed
built_up_area is highly skewed (γ1 = 40.77881958)Skewed
carpet_area is highly skewed (γ1 = 24.33323909)Skewed
floorNum has 129 (3.5%) zerosZeros
luxury_score has 462 (12.6%) zerosZeros

Reproduction

Analysis started2023-09-12 16:49:03.943596
Analysis finished2023-09-12 16:49:25.651791
Duration21.71 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

property_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.6 KiB
flat
2818 
house
859 

Length

Max length5
Median length4
Mean length4.2336144
Min length4

Characters and Unicode

Total characters15567
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowflat
2nd rowflat
3rd rowflat
4th rowflat
5th rowhouse

Common Values

ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Length

2023-09-12T22:19:25.765801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:25.924813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2818
76.6%
house 859
 
23.4%

Most occurring characters

ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15567
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 15567
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15567
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2818
18.1%
l 2818
18.1%
a 2818
18.1%
t 2818
18.1%
h 859
 
5.5%
o 859
 
5.5%
u 859
 
5.5%
s 859
 
5.5%
e 859
 
5.5%

society
Categorical

Distinct676
Distinct (%)18.4%
Missing1
Missing (%)< 0.1%
Memory size293.9 KiB
independent
486 
tulip violet
 
75
ss the leaf
 
73
shapoorji pallonji joyville gurugram
 
42
dlf new town heights
 
42
Other values (671)
2958 

Length

Max length49
Median length39
Mean length16.869695
Min length1

Characters and Unicode

Total characters62013
Distinct characters41
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique308 ?
Unique (%)8.4%

Sample

1st rowsignature global orchard avenue
2nd rowzara aavaas
3rd rowats triumph
4th rowexperion the heartsong
5th rowunitech deerwood chase

Common Values

ValueCountFrequency (%)
independent 486
 
13.2%
tulip violet 75
 
2.0%
ss the leaf 73
 
2.0%
shapoorji pallonji joyville gurugram 42
 
1.1%
dlf new town heights 42
 
1.1%
signature global park 35
 
1.0%
shree vardhman victoria 34
 
0.9%
smart world orchard 32
 
0.9%
emaar mgf emerald floors premier 32
 
0.9%
paras dews 31
 
0.8%
Other values (666) 2794
76.0%

Length

2023-09-12T22:19:26.115828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
independent 491
 
5.1%
the 350
 
3.6%
dlf 220
 
2.3%
park 209
 
2.2%
city 166
 
1.7%
emaar 155
 
1.6%
global 153
 
1.6%
m3m 152
 
1.6%
signature 150
 
1.6%
heights 134
 
1.4%
Other values (783) 7497
77.5%

Most occurring characters

ValueCountFrequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 55465
89.4%
Space Separator 6003
 
9.7%
Decimal Number 527
 
0.8%
Other Punctuation 10
 
< 0.1%
Dash Punctuation 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Decimal Number
ValueCountFrequency (%)
3 207
39.3%
2 82
 
15.6%
1 75
 
14.2%
6 56
 
10.6%
8 32
 
6.1%
4 19
 
3.6%
5 17
 
3.2%
9 13
 
2.5%
0 13
 
2.5%
7 13
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 7
70.0%
/ 2
 
20.0%
. 1
 
10.0%
Space Separator
ValueCountFrequency (%)
6003
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 55465
89.4%
Common 6548
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6710
12.1%
a 5861
 
10.6%
r 4171
 
7.5%
n 4163
 
7.5%
i 3830
 
6.9%
t 3719
 
6.7%
s 3472
 
6.3%
l 2943
 
5.3%
o 2755
 
5.0%
d 2488
 
4.5%
Other values (16) 15353
27.7%
Common
ValueCountFrequency (%)
6003
91.7%
3 207
 
3.2%
2 82
 
1.3%
1 75
 
1.1%
6 56
 
0.9%
8 32
 
0.5%
4 19
 
0.3%
5 17
 
0.3%
9 13
 
0.2%
0 13
 
0.2%
Other values (5) 31
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6710
 
10.8%
6003
 
9.7%
a 5861
 
9.5%
r 4171
 
6.7%
n 4163
 
6.7%
i 3830
 
6.2%
t 3719
 
6.0%
s 3472
 
5.6%
l 2943
 
4.7%
o 2755
 
4.4%
Other values (31) 18386
29.6%

sector
Categorical

Distinct113
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size266.9 KiB
sohna road
 
154
sector 85
 
108
sector 102
 
107
sector 92
 
100
sector 69
 
93
Other values (108)
3115 

Length

Max length26
Median length9
Mean length9.3209138
Min length7

Characters and Unicode

Total characters34273
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowsector 93
2nd rowsector 104
3rd rowsector 104
4th rowsector 108
5th rowsector 50

Common Values

ValueCountFrequency (%)
sohna road 154
 
4.2%
sector 85 108
 
2.9%
sector 102 107
 
2.9%
sector 92 100
 
2.7%
sector 69 93
 
2.5%
sector 90 89
 
2.4%
sector 65 87
 
2.4%
sector 81 87
 
2.4%
sector 109 86
 
2.3%
sector 79 76
 
2.1%
Other values (103) 2690
73.2%

Length

2023-09-12T22:19:26.266838image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sector 3452
46.8%
road 178
 
2.4%
sohna 166
 
2.2%
85 108
 
1.5%
102 107
 
1.4%
92 100
 
1.4%
69 93
 
1.3%
90 89
 
1.2%
65 87
 
1.2%
81 87
 
1.2%
Other values (106) 2915
39.5%

Most occurring characters

ValueCountFrequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23299
68.0%
Decimal Number 7269
 
21.2%
Space Separator 3705
 
10.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 1076
14.8%
0 804
11.1%
8 780
10.7%
9 764
10.5%
6 742
10.2%
7 684
9.4%
2 676
9.3%
3 666
9.2%
5 593
8.2%
4 484
6.7%
Space Separator
ValueCountFrequency (%)
3705
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23299
68.0%
Common 10974
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 3807
16.3%
s 3697
15.9%
r 3697
15.9%
e 3542
15.2%
c 3503
15.0%
t 3463
14.9%
a 699
 
3.0%
d 249
 
1.1%
n 221
 
0.9%
h 203
 
0.9%
Other values (10) 218
 
0.9%
Common
ValueCountFrequency (%)
3705
33.8%
1 1076
 
9.8%
0 804
 
7.3%
8 780
 
7.1%
9 764
 
7.0%
6 742
 
6.8%
7 684
 
6.2%
2 676
 
6.2%
3 666
 
6.1%
5 593
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 3807
11.1%
3705
10.8%
s 3697
10.8%
r 3697
10.8%
e 3542
10.3%
c 3503
10.2%
t 3463
10.1%
1 1076
 
3.1%
0 804
 
2.3%
8 780
 
2.3%
Other values (21) 6199
18.1%

price
Real number (ℝ)

Distinct473
Distinct (%)12.9%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.5336639
Minimum0.07
Maximum31.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:26.403848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.07
5-th percentile0.37
Q10.95
median1.52
Q32.75
95-th percentile8.5
Maximum31.5
Range31.43
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation2.9806235
Coefficient of variation (CV)1.1764084
Kurtosis14.933373
Mean2.5336639
Median Absolute Deviation (MAD)0.72
Skewness3.2791705
Sum9273.21
Variance8.8841164
MonotonicityNot monotonic
2023-09-12T22:19:26.543859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 80
 
2.2%
1.2 64
 
1.7%
1.5 64
 
1.7%
0.9 63
 
1.7%
1.1 62
 
1.7%
1.4 60
 
1.6%
1.3 57
 
1.6%
2 52
 
1.4%
0.95 52
 
1.4%
1.6 48
 
1.3%
Other values (463) 3058
83.2%
ValueCountFrequency (%)
0.07 1
 
< 0.1%
0.16 1
 
< 0.1%
0.17 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 8
0.2%
0.21 6
0.2%
0.22 8
0.2%
0.23 1
 
< 0.1%
0.24 6
0.2%
0.25 11
0.3%
ValueCountFrequency (%)
31.5 1
 
< 0.1%
27.5 1
 
< 0.1%
26 2
0.1%
25 1
 
< 0.1%
24 1
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
20 3
0.1%
19.5 2
0.1%
19 3
0.1%

price_per_sqft
Real number (ℝ)

Distinct2651
Distinct (%)72.4%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13892.668
Minimum4
Maximum600000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:26.684869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4715.95
Q16817.25
median9020
Q313880.5
95-th percentile33333
Maximum600000
Range599996
Interquartile range (IQR)7063.25

Descriptive statistics

Standard deviation23210.067
Coefficient of variation (CV)1.6706702
Kurtosis186.92801
Mean13892.668
Median Absolute Deviation (MAD)2794
Skewness11.43719
Sum50847166
Variance5.3870722 × 108
MonotonicityNot monotonic
2023-09-12T22:19:26.821576image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 27
 
0.7%
8000 19
 
0.5%
5000 17
 
0.5%
12500 14
 
0.4%
6666 13
 
0.4%
22222 13
 
0.4%
11111 13
 
0.4%
8333 12
 
0.3%
7500 12
 
0.3%
6000 11
 
0.3%
Other values (2641) 3509
95.4%
(Missing) 17
 
0.5%
ValueCountFrequency (%)
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
53 1
< 0.1%
57 1
< 0.1%
58 2
0.1%
60 1
< 0.1%
61 1
< 0.1%
79 1
< 0.1%
ValueCountFrequency (%)
600000 1
< 0.1%
400000 1
< 0.1%
315789 1
< 0.1%
308333 1
< 0.1%
290948 1
< 0.1%
283333 1
< 0.1%
266666 1
< 0.1%
261194 1
< 0.1%
245398 1
< 0.1%
241666 1
< 0.1%

area
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1312
Distinct (%)35.8%
Missing17
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2888.3311
Minimum50
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:26.972092image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile518.85
Q11232.25
median1733
Q32300
95-th percentile4246.2
Maximum875000
Range874950
Interquartile range (IQR)1067.75

Descriptive statistics

Standard deviation23167.506
Coefficient of variation (CV)8.0210699
Kurtosis942.02903
Mean2888.3311
Median Absolute Deviation (MAD)533
Skewness29.730956
Sum10571292
Variance5.3673333 × 108
MonotonicityNot monotonic
2023-09-12T22:19:27.115103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1650 54
 
1.5%
1350 48
 
1.3%
1800 47
 
1.3%
1950 43
 
1.2%
3240 43
 
1.2%
2700 39
 
1.1%
900 38
 
1.0%
2000 33
 
0.9%
2250 25
 
0.7%
2400 23
 
0.6%
Other values (1302) 3267
88.8%
ValueCountFrequency (%)
50 4
0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
60 2
0.1%
61 1
 
< 0.1%
67 2
0.1%
70 1
 
< 0.1%
72 1
 
< 0.1%
76 1
 
< 0.1%
ValueCountFrequency (%)
875000 1
< 0.1%
642857 1
< 0.1%
620000 1
< 0.1%
566667 1
< 0.1%
215517 1
< 0.1%
98978 1
< 0.1%
82781 1
< 0.1%
65517 2
0.1%
65261 1
< 0.1%
58228 1
< 0.1%

areaWithType
Categorical

Distinct2355
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size428.2 KiB
Plot area 360(301.01 sq.m.)
 
37
Plot area 300(250.84 sq.m.)
 
26
Plot area 502(419.74 sq.m.)
 
19
Plot area 200(167.23 sq.m.)
 
19
Plot area 270(225.75 sq.m.)
 
17
Other values (2350)
3559 

Length

Max length124
Median length119
Mean length54.236062
Min length12

Characters and Unicode

Total characters199426
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1849 ?
Unique (%)50.3%

Sample

1st rowCarpet area: 543.53 (50.5 sq.m.)
2nd rowSuper Built up area 569(52.86 sq.m.)
3rd rowSuper Built up area 3150(292.64 sq.m.)Carpet area: 1950 sq.ft. (181.16 sq.m.)
4th rowSuper Built up area 2003(186.08 sq.m.)Built Up area: 1771.32 sq.ft. (164.56 sq.m.)Carpet area: 1302.01 sq.ft. (120.96 sq.m.)
5th rowPlot area 359(33.35 sq.m.)

Common Values

ValueCountFrequency (%)
Plot area 360(301.01 sq.m.) 37
 
1.0%
Plot area 300(250.84 sq.m.) 26
 
0.7%
Plot area 502(419.74 sq.m.) 19
 
0.5%
Plot area 200(167.23 sq.m.) 19
 
0.5%
Plot area 270(225.75 sq.m.) 17
 
0.5%
Super Built up area 1578(146.6 sq.m.) 17
 
0.5%
Super Built up area 1950(181.16 sq.m.)Carpet area: 1161 sq.ft. (107.86 sq.m.) 17
 
0.5%
Super Built up area 1350(125.42 sq.m.) 15
 
0.4%
Plot area 500(418.06 sq.m.) 14
 
0.4%
Plot area 150(125.42 sq.m.) 14
 
0.4%
Other values (2345) 3482
94.7%

Length

2023-09-12T22:19:27.267113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
area 5573
18.5%
sq.m 3655
12.1%
up 3020
 
10.0%
built 2316
 
7.7%
super 1875
 
6.2%
sq.ft 1751
 
5.8%
sq.m.)carpet 1185
 
3.9%
sq.m.)built 702
 
2.3%
carpet 683
 
2.3%
plot 681
 
2.3%
Other values (2846) 8700
28.9%

Most occurring characters

ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82758
41.5%
Decimal Number 47135
23.6%
Space Separator 26464
 
13.3%
Other Punctuation 23406
 
11.7%
Uppercase Letter 8593
 
4.3%
Close Punctuation 5535
 
2.8%
Open Punctuation 5535
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 13154
15.9%
r 9456
11.4%
e 9320
11.3%
s 7567
9.1%
q 7431
9.0%
t 7324
8.8%
u 6770
8.2%
p 6767
8.2%
m 5544
6.7%
l 3701
 
4.5%
Other values (5) 5724
6.9%
Decimal Number
ValueCountFrequency (%)
1 9205
19.5%
0 6628
14.1%
2 5688
12.1%
5 4714
10.0%
3 3960
8.4%
4 3711
7.9%
6 3674
 
7.8%
7 3254
 
6.9%
8 3157
 
6.7%
9 3144
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
B 3020
35.1%
S 1875
21.8%
C 1872
21.8%
U 1145
 
13.3%
P 681
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 20389
87.1%
: 3017
 
12.9%
Space Separator
ValueCountFrequency (%)
26464
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5535
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108075
54.2%
Latin 91351
45.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 13154
14.4%
r 9456
10.4%
e 9320
10.2%
s 7567
8.3%
q 7431
8.1%
t 7324
8.0%
u 6770
7.4%
p 6767
7.4%
m 5544
 
6.1%
l 3701
 
4.1%
Other values (10) 14317
15.7%
Common
ValueCountFrequency (%)
26464
24.5%
. 20389
18.9%
1 9205
 
8.5%
0 6628
 
6.1%
2 5688
 
5.3%
) 5535
 
5.1%
( 5535
 
5.1%
5 4714
 
4.4%
3 3960
 
3.7%
4 3711
 
3.4%
Other values (5) 16246
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 199426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26464
 
13.3%
. 20389
 
10.2%
a 13154
 
6.6%
r 9456
 
4.7%
e 9320
 
4.7%
1 9205
 
4.6%
s 7567
 
3.8%
q 7431
 
3.7%
t 7324
 
3.7%
u 6770
 
3.4%
Other values (25) 82346
41.3%

bedRoom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3600761
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:27.388124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8976289
Coefficient of variation (CV)0.56475771
Kurtosis18.212873
Mean3.3600761
Median Absolute Deviation (MAD)1
Skewness3.4851418
Sum12355
Variance3.6009954
MonotonicityNot monotonic
2023-09-12T22:19:27.681145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1496
40.7%
2 942
25.6%
4 660
17.9%
5 210
 
5.7%
1 124
 
3.4%
6 74
 
2.0%
9 41
 
1.1%
8 30
 
0.8%
7 28
 
0.8%
12 28
 
0.8%
Other values (9) 44
 
1.2%
ValueCountFrequency (%)
1 124
 
3.4%
2 942
25.6%
3 1496
40.7%
4 660
17.9%
5 210
 
5.7%
6 74
 
2.0%
7 28
 
0.8%
8 30
 
0.8%
9 41
 
1.1%
10 20
 
0.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 2
 
0.1%
18 2
 
0.1%
16 12
0.3%
14 1
 
< 0.1%
13 4
 
0.1%
12 28
0.8%
11 1
 
< 0.1%
10 20
0.5%

bathroom
Real number (ℝ)

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4245309
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:27.796154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile6
Maximum21
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9480681
Coefficient of variation (CV)0.56885693
Kurtosis17.542297
Mean3.4245309
Median Absolute Deviation (MAD)1
Skewness3.2488298
Sum12592
Variance3.7949693
MonotonicityNot monotonic
2023-09-12T22:19:27.900161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1077
29.3%
2 1047
28.5%
4 820
22.3%
5 294
 
8.0%
1 156
 
4.2%
6 117
 
3.2%
9 41
 
1.1%
7 40
 
1.1%
8 25
 
0.7%
12 22
 
0.6%
Other values (9) 38
 
1.0%
ValueCountFrequency (%)
1 156
 
4.2%
2 1047
28.5%
3 1077
29.3%
4 820
22.3%
5 294
 
8.0%
6 117
 
3.2%
7 40
 
1.1%
8 25
 
0.7%
9 41
 
1.1%
10 9
 
0.2%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 3
 
0.1%
18 4
 
0.1%
17 3
 
0.1%
16 8
 
0.2%
14 2
 
0.1%
13 4
 
0.1%
12 22
0.6%
11 4
 
0.1%
10 9
0.2%

balcony
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size238.2 KiB
3+
1172 
3
1074 
2
884 
1
365 
No
 
96

Length

Max length2
Median length1
Mean length1.3448463
Min length1

Characters and Unicode

Total characters4945
Distinct characters7
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row3+
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3+ 1172
31.9%
3 1074
29.2%
2 884
24.0%
1 365
 
9.9%
No 96
 
2.6%
0 86
 
2.3%

Length

2023-09-12T22:19:28.022170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:28.142180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
3 2246
61.1%
2 884
 
24.0%
1 365
 
9.9%
no 96
 
2.6%
0 86
 
2.3%

Most occurring characters

ValueCountFrequency (%)
3 2246
45.4%
+ 1172
23.7%
2 884
 
17.9%
1 365
 
7.4%
N 96
 
1.9%
o 96
 
1.9%
0 86
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3581
72.4%
Math Symbol 1172
 
23.7%
Uppercase Letter 96
 
1.9%
Lowercase Letter 96
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2246
62.7%
2 884
 
24.7%
1 365
 
10.2%
0 86
 
2.4%
Math Symbol
ValueCountFrequency (%)
+ 1172
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 96
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4753
96.1%
Latin 192
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2246
47.3%
+ 1172
24.7%
2 884
 
18.6%
1 365
 
7.7%
0 86
 
1.8%
Latin
ValueCountFrequency (%)
N 96
50.0%
o 96
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2246
45.4%
+ 1172
23.7%
2 884
 
17.9%
1 365
 
7.4%
N 96
 
1.9%
o 96
 
1.9%
0 86
 
1.7%

floorNum
Real number (ℝ)

Distinct43
Distinct (%)1.2%
Missing19
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean6.7982504
Minimum0
Maximum51
Zeros129
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:28.263188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q310
95-th percentile18
Maximum51
Range51
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.0124542
Coefficient of variation (CV)0.884412
Kurtosis4.5153928
Mean6.7982504
Median Absolute Deviation (MAD)3
Skewness1.6936988
Sum24868
Variance36.149606
MonotonicityNot monotonic
2023-09-12T22:19:28.403198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 498
13.5%
2 493
13.4%
1 351
 
9.5%
4 316
 
8.6%
8 195
 
5.3%
6 183
 
5.0%
10 179
 
4.9%
7 176
 
4.8%
5 169
 
4.6%
9 161
 
4.4%
Other values (33) 937
25.5%
ValueCountFrequency (%)
0 129
 
3.5%
1 351
9.5%
2 493
13.4%
3 498
13.5%
4 316
8.6%
5 169
 
4.6%
6 183
 
5.0%
7 176
 
4.8%
8 195
 
5.3%
9 161
 
4.4%
ValueCountFrequency (%)
51 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
43 2
0.1%
40 1
 
< 0.1%
39 2
0.1%
38 1
 
< 0.1%
35 2
0.1%
34 2
0.1%
33 4
0.1%

facing
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.3%
Missing1045
Missing (%)28.4%
Memory size225.5 KiB
East
623 
North-East
623 
North
387 
West
249 
South
231 
Other values (3)
519 

Length

Max length10
Median length5
Mean length6.8381459
Min length4

Characters and Unicode

Total characters17998
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEast
2nd rowNorth-West
3rd rowNorth-East
4th rowSouth-East
5th rowEast

Common Values

ValueCountFrequency (%)
East 623
16.9%
North-East 623
16.9%
North 387
 
10.5%
West 249
 
6.8%
South 231
 
6.3%
North-West 193
 
5.2%
South-East 173
 
4.7%
South-West 153
 
4.2%
(Missing) 1045
28.4%

Length

2023-09-12T22:19:28.525206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:28.652215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
east 623
23.7%
north-east 623
23.7%
north 387
14.7%
west 249
 
9.5%
south 231
 
8.8%
north-west 193
 
7.3%
south-east 173
 
6.6%
south-west 153
 
5.8%

Most occurring characters

ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13082
72.7%
Uppercase Letter 3774
 
21.0%
Dash Punctuation 1142
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 3774
28.8%
s 2014
15.4%
o 1760
13.5%
h 1760
13.5%
a 1419
 
10.8%
r 1203
 
9.2%
e 595
 
4.5%
u 557
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
E 1419
37.6%
N 1203
31.9%
W 595
15.8%
S 557
 
14.8%
Dash Punctuation
ValueCountFrequency (%)
- 1142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16856
93.7%
Common 1142
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 3774
22.4%
s 2014
11.9%
o 1760
10.4%
h 1760
10.4%
E 1419
 
8.4%
a 1419
 
8.4%
N 1203
 
7.1%
r 1203
 
7.1%
W 595
 
3.5%
e 595
 
3.5%
Other values (2) 1114
 
6.6%
Common
ValueCountFrequency (%)
- 1142
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17998
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 3774
21.0%
s 2014
11.2%
o 1760
9.8%
h 1760
9.8%
E 1419
 
7.9%
a 1419
 
7.9%
N 1203
 
6.7%
r 1203
 
6.7%
- 1142
 
6.3%
W 595
 
3.3%
Other values (3) 1709
9.5%

agePossession
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size280.2 KiB
Relatively New
1646 
New Property
593 
Moderately Old
563 
Undefined
446 
Old Property
303 

Length

Max length18
Median length14
Mean length13.043242
Min length9

Characters and Unicode

Total characters47960
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew Property
2nd rowRelatively New
3rd rowRelatively New
4th rowModerately Old
5th rowOld Property

Common Values

ValueCountFrequency (%)
Relatively New 1646
44.8%
New Property 593
 
16.1%
Moderately Old 563
 
15.3%
Undefined 446
 
12.1%
Old Property 303
 
8.2%
Under Construction 126
 
3.4%

Length

2023-09-12T22:19:28.778225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:28.895234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
new 2239
32.4%
relatively 1646
23.8%
property 896
13.0%
old 866
 
12.5%
moderately 563
 
8.1%
undefined 446
 
6.5%
under 126
 
1.8%
construction 126
 
1.8%

Most occurring characters

ValueCountFrequency (%)
e 8571
17.9%
l 4721
 
9.8%
t 3357
 
7.0%
3231
 
6.7%
y 3105
 
6.5%
r 2607
 
5.4%
d 2447
 
5.1%
N 2239
 
4.7%
w 2239
 
4.7%
i 2218
 
4.6%
Other values (15) 13225
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 37821
78.9%
Uppercase Letter 6908
 
14.4%
Space Separator 3231
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8571
22.7%
l 4721
12.5%
t 3357
 
8.9%
y 3105
 
8.2%
r 2607
 
6.9%
d 2447
 
6.5%
w 2239
 
5.9%
i 2218
 
5.9%
a 2209
 
5.8%
o 1711
 
4.5%
Other values (7) 4636
12.3%
Uppercase Letter
ValueCountFrequency (%)
N 2239
32.4%
R 1646
23.8%
P 896
13.0%
O 866
 
12.5%
U 572
 
8.3%
M 563
 
8.1%
C 126
 
1.8%
Space Separator
ValueCountFrequency (%)
3231
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44729
93.3%
Common 3231
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8571
19.2%
l 4721
10.6%
t 3357
 
7.5%
y 3105
 
6.9%
r 2607
 
5.8%
d 2447
 
5.5%
N 2239
 
5.0%
w 2239
 
5.0%
i 2218
 
5.0%
a 2209
 
4.9%
Other values (14) 11016
24.6%
Common
ValueCountFrequency (%)
3231
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8571
17.9%
l 4721
 
9.8%
t 3357
 
7.0%
3231
 
6.7%
y 3105
 
6.5%
r 2607
 
5.4%
d 2447
 
5.1%
N 2239
 
4.7%
w 2239
 
4.7%
i 2218
 
4.6%
Other values (15) 13225
27.6%

super_built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct593
Distinct (%)31.6%
Missing1802
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1925.2376
Minimum89
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:29.044244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile767
Q11479.5
median1828
Q32215
95-th percentile3185
Maximum10000
Range9911
Interquartile range (IQR)735.5

Descriptive statistics

Standard deviation764.17218
Coefficient of variation (CV)0.39692356
Kurtosis10.349191
Mean1925.2376
Median Absolute Deviation (MAD)372
Skewness1.8364563
Sum3609820.6
Variance583959.12
MonotonicityNot monotonic
2023-09-12T22:19:29.190255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1950 37
 
1.0%
1650 37
 
1.0%
2000 25
 
0.7%
1578 25
 
0.7%
1640 22
 
0.6%
2150 22
 
0.6%
2408 19
 
0.5%
1900 19
 
0.5%
1930 18
 
0.5%
2812 17
 
0.5%
Other values (583) 1634
44.4%
(Missing) 1802
49.0%
ValueCountFrequency (%)
89 1
< 0.1%
145 1
< 0.1%
161 1
< 0.1%
215 1
< 0.1%
216 1
< 0.1%
325 1
< 0.1%
340 1
< 0.1%
352 1
< 0.1%
380 1
< 0.1%
406 1
< 0.1%
ValueCountFrequency (%)
10000 1
< 0.1%
6926 1
< 0.1%
6000 1
< 0.1%
5800 2
0.1%
5514 1
< 0.1%
5350 2
0.1%
5200 2
0.1%
4890 1
< 0.1%
4857 1
< 0.1%
4848 2
0.1%

built_up_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct624
Distinct (%)36.9%
Missing1987
Missing (%)54.0%
Infinite0
Infinite (%)0.0%
Mean1841.9314
Minimum2
Maximum737147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:29.333267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile129.681
Q1360
median1256.5
Q31900
95-th percentile3936.5
Maximum737147
Range737145
Interquartile range (IQR)1540

Descriptive statistics

Standard deviation17945.374
Coefficient of variation (CV)9.7426943
Kurtosis1671.8347
Mean1841.9314
Median Absolute Deviation (MAD)754.5
Skewness40.77882
Sum3112864
Variance3.2203646 × 108
MonotonicityNot monotonic
2023-09-12T22:19:29.503281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 45
 
1.2%
300 34
 
0.9%
1900 34
 
0.9%
1600 26
 
0.7%
1300 24
 
0.7%
2000 24
 
0.7%
1700 23
 
0.6%
1800 22
 
0.6%
200 22
 
0.6%
900 21
 
0.6%
Other values (614) 1415
38.5%
(Missing) 1987
54.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
14 1
 
< 0.1%
30 1
 
< 0.1%
33 1
 
< 0.1%
40 4
0.1%
50 6
0.2%
53 1
 
< 0.1%
55 1
 
< 0.1%
56 1
 
< 0.1%
57 1
 
< 0.1%
ValueCountFrequency (%)
737147 1
 
< 0.1%
9500 1
 
< 0.1%
9000 4
0.1%
8286 1
 
< 0.1%
8000 1
 
< 0.1%
7500 2
 
0.1%
7450 1
 
< 0.1%
7331 2
 
0.1%
7000 9
0.2%
6500 1
 
< 0.1%

carpet_area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct733
Distinct (%)39.2%
Missing1805
Missing (%)49.1%
Infinite0
Infinite (%)0.0%
Mean2529.1795
Minimum15
Maximum607936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:29.694294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile350
Q1843
median1300
Q31790
95-th percentile2950
Maximum607936
Range607921
Interquartile range (IQR)947

Descriptive statistics

Standard deviation22799.836
Coefficient of variation (CV)9.0147166
Kurtosis604.53764
Mean2529.1795
Median Absolute Deviation (MAD)472.5
Skewness24.333239
Sum4734624
Variance5.1983254 × 108
MonotonicityNot monotonic
2023-09-12T22:19:29.895228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1400 42
 
1.1%
1600 35
 
1.0%
1800 35
 
1.0%
1200 31
 
0.8%
1500 29
 
0.8%
1650 28
 
0.8%
1350 27
 
0.7%
1300 23
 
0.6%
1000 22
 
0.6%
1450 22
 
0.6%
Other values (723) 1578
42.9%
(Missing) 1805
49.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
33 1
 
< 0.1%
48 1
 
< 0.1%
50 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
66 1
 
< 0.1%
72 1
 
< 0.1%
76.44 3
0.1%
77.31 1
 
< 0.1%
ValueCountFrequency (%)
607936 1
< 0.1%
569243 1
< 0.1%
514396 1
< 0.1%
64529 1
< 0.1%
64412 1
< 0.1%
58141 1
< 0.1%
54917 1
< 0.1%
48811 1
< 0.1%
45966 1
< 0.1%
34401 1
< 0.1%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2972 
1
705 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Length

2023-09-12T22:19:30.045390image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:30.161397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring characters

ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2972
80.8%
1 705
 
19.2%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2349 
1
1328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Length

2023-09-12T22:19:30.258404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:30.373413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring characters

ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2349
63.9%
1 1328
36.1%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3339 
1
338 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Length

2023-09-12T22:19:30.469419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:30.585178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3339
90.8%
1 338
 
9.2%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3021 
1
656 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Length

2023-09-12T22:19:30.867199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:30.975207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3021
82.2%
1 656
 
17.8%

others
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
3272 
1
405 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Length

2023-09-12T22:19:31.080215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:31.189222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3272
89.0%
1 405
 
11.0%

furnishing_type
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size237.0 KiB
0
2404 
2
1061 
1
 
212

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 2404
65.4%
2 1061
28.9%
1 212
 
5.8%

Length

2023-09-12T22:19:31.286230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-12T22:19:31.396238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2404
65.4%
2 1061
28.9%
1 212
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 2404
65.4%
2 1061
28.9%
1 212
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3677
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2404
65.4%
2 1061
28.9%
1 212
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3677
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2404
65.4%
2 1061
28.9%
1 212
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2404
65.4%
2 1061
28.9%
1 212
 
5.8%

luxury_score
Real number (ℝ)

Distinct161
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.512918
Minimum0
Maximum174
Zeros462
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size57.5 KiB
2023-09-12T22:19:31.511247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q131
median59
Q3110
95-th percentile174
Maximum174
Range174
Interquartile range (IQR)79

Descriptive statistics

Standard deviation53.059082
Coefficient of variation (CV)0.74195102
Kurtosis-0.88020421
Mean71.512918
Median Absolute Deviation (MAD)38
Skewness0.4590463
Sum262953
Variance2815.2662
MonotonicityNot monotonic
2023-09-12T22:19:31.643258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 462
 
12.6%
49 348
 
9.5%
174 195
 
5.3%
44 60
 
1.6%
165 55
 
1.5%
38 55
 
1.5%
72 52
 
1.4%
60 47
 
1.3%
37 45
 
1.2%
42 45
 
1.2%
Other values (151) 2313
62.9%
ValueCountFrequency (%)
0 462
12.6%
5 6
 
0.2%
6 6
 
0.2%
7 41
 
1.1%
8 30
 
0.8%
9 9
 
0.2%
12 6
 
0.2%
13 10
 
0.3%
14 12
 
0.3%
15 43
 
1.2%
ValueCountFrequency (%)
174 195
5.3%
169 1
 
< 0.1%
168 9
 
0.2%
167 21
 
0.6%
166 10
 
0.3%
165 55
 
1.5%
161 3
 
0.1%
160 28
 
0.8%
159 23
 
0.6%
158 34
 
0.9%

Interactions

2023-09-12T22:19:22.852607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:06.896416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.033360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.529641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:12.332774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:14.982242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.528357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:18.018241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:19.765379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.350500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:22.988617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:07.200074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.184376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.690654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:12.641799image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:15.131254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.682369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:18.217254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:19.907392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.513509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:23.133632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:07.475097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.340383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.825665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:12.925821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:15.296268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.819380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:18.370267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:20.049403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.667521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:23.297644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:07.752113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.492395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.946670image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:13.150837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:15.453279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.941387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:18.514277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:20.224416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.813533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:23.468654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:07.994283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.658407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:11.085682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:13.486870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:15.624291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:17.075397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:18.668288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:20.409429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.956546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:23.644666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:08.165296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.796416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:11.234691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:13.818165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:15.784304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:17.249558image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:18.817299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:20.606441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:22.095557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:23.974690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:08.455318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:09.933426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:11.383702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:14.371198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:15.917312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:17.410194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:19.142323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:20.763452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:22.241563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:24.110706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:08.615329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.063607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:11.532716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:14.525212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.049324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:17.572209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:19.298336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:20.904467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:22.391574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:24.276713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:08.762340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.212616image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:11.715730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:14.706223image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.207334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:17.735221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:19.451752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.045474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:22.548590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:24.435724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:08.899350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:10.363629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:12.043755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:14.845232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:16.359346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:17.883231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:19.606369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:21.185486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-09-12T22:19:22.711600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-09-12T22:19:31.780267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
priceprice_per_sqftareabedRoombathroomfloorNumsuper_built_up_areabuilt_up_areacarpet_arealuxury_scoreproperty_typebalconyfacingagePossessionstudy roomservant roomstore roompooja roomothersfurnishing_type
price1.0000.7440.7440.6810.7200.0010.772-0.0010.6130.2150.5430.1210.0210.1020.2440.3690.3030.3340.0340.175
price_per_sqft0.7441.0000.2070.4170.411-0.1260.287-0.4010.1360.0540.2010.0500.0000.0460.0300.0440.0000.0430.0360.022
area0.7440.2071.0000.6240.6870.1160.9480.3920.8010.2590.0280.0000.0220.0000.0180.0150.0390.0370.0420.042
bedRoom0.6810.4170.6241.0000.862-0.1040.800-0.1350.5690.0570.5950.1600.0320.1260.1540.3170.2230.2910.0790.168
bathroom0.7200.4110.6870.8621.000-0.0050.819-0.0190.5990.1790.4720.2010.0440.1090.1760.5200.2440.2860.0700.200
floorNum0.001-0.1260.116-0.104-0.0051.0000.1520.3490.1590.2320.4850.0790.0000.1220.0780.0840.1120.1020.0330.022
super_built_up_area0.7720.2870.9480.8000.8190.1521.0000.9260.8940.2221.0000.3060.0000.0800.1210.5840.0460.1570.0840.133
built_up_area-0.001-0.4010.392-0.135-0.0190.3490.9261.0000.9690.2630.0000.0001.0000.0000.0000.0000.0000.0000.0000.086
carpet_area0.6130.1360.8010.5690.5990.1590.8940.9691.0000.2390.0000.0120.0000.0000.0030.0000.0000.0000.0160.000
luxury_score0.2150.0540.2590.0570.1790.2320.2220.2630.2391.0000.3290.2080.0650.2240.1830.3470.2280.1890.1760.244
property_type0.5430.2010.0280.5950.4720.4851.0000.0000.0000.3291.0000.3380.0940.3540.1280.0650.2410.2520.0260.078
balcony0.1210.0500.0000.1600.2010.0790.3060.0000.0120.2080.3381.0000.0000.2050.1820.4400.1450.1960.0800.178
facing0.0210.0000.0220.0320.0440.0000.0001.0000.0000.0650.0940.0001.0000.0900.0000.0360.0360.0290.0000.048
agePossession0.1020.0460.0000.1260.1090.1220.0800.0000.0000.2240.3540.2050.0901.0000.1110.2820.1410.1840.1070.214
study room0.2440.0300.0180.1540.1760.0780.1210.0000.0030.1830.1280.1820.0000.1111.0000.1850.2260.3130.0310.142
servant room0.3690.0440.0150.3170.5200.0840.5840.0000.0000.3470.0650.4400.0360.2820.1851.0000.1610.2520.0000.273
store room0.3030.0000.0390.2230.2440.1120.0460.0000.0000.2280.2410.1450.0360.1410.2260.1611.0000.3050.1060.157
pooja room0.3340.0430.0370.2910.2860.1020.1570.0000.0000.1890.2520.1960.0290.1840.3130.2520.3051.0000.0330.217
others0.0340.0360.0420.0790.0700.0330.0840.0000.0160.1760.0260.0800.0000.1070.0310.0000.1060.0331.0000.062
furnishing_type0.1750.0220.0420.1680.2000.0220.1330.0860.0000.2440.0780.1780.0480.2140.1420.2730.1570.2170.0621.000

Missing values

2023-09-12T22:19:24.716513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-12T22:19:25.088537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-12T22:19:25.440777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
0flatsignature global orchard avenuesector 930.407359.0544.0Carpet area: 543.53 (50.5 sq.m.)22211.0NaNNew PropertyNaNNaN543.5769500000037
1flatzara aavaassector 1040.407029.0569.0Super Built up area 569(52.86 sq.m.)22114.0NaNRelatively New569.0NaNNaN00000038
2flatats triumphsector 1042.5513076.01950.0Super Built up area 3150(292.64 sq.m.)Carpet area: 1950 sq.ft. (181.16 sq.m.)443+21.0EastRelatively New3150.0NaN1950.00000010000150
3flatexperion the heartsongsector 1081.658237.02003.0Super Built up area 2003(186.08 sq.m.)Built Up area: 1771.32 sq.ft. (164.56 sq.m.)Carpet area: 1302.01 sq.ft. (120.96 sq.m.)3433.0North-WestModerately Old2003.01771.321302.0100010000275
4houseunitech deerwood chasesector 508.45235376.0359.0Plot area 359(33.35 sq.m.)3322.0North-EastOld PropertyNaN359.00NaN110002102
5flatsmart world one dxpsector 1133.4513269.02600.0Super Built up area 2600(241.55 sq.m.)44311.0South-EastUndefined2600.0NaNNaN110002156
6flatsobha citysector 1081.9013758.01381.0Super Built up area 1381(128.3 sq.m.)2228.0EastRelatively New1381.0NaNNaN00000085
7flatm3m woodshiresector 1070.826002.01366.0Super Built up area 1366(126.91 sq.m.)223+7.0North-EastRelatively New1366.0NaNNaN000000111
8housemy homesector 1100.3412592.0270.0Plot area 270(25.08 sq.m.)2223.0WestModerately OldNaN270.00NaN00000022
9houseindependentsector 313.2522429.01449.0Plot area 161(134.62 sq.m.)443+3.0NaNModerately OldNaN161.00NaN11110229
property_typesocietysectorpriceprice_per_sqftareaareaWithTypebedRoombathroombalconyfloorNumfacingagePossessionsuper_built_up_areabuilt_up_areacarpet_areastudy roomservant roomstore roompooja roomothersfurnishing_typeluxury_score
3792flatsbtl caladiumsector 1091.606286.02545.0Super Built up area 2545(236.44 sq.m.)3339.0NorthRelatively New2545.0NaNNaN01000075
3793flatraheja sampadasector 920.704453.01572.0Built Up area: 1572 (146.04 sq.m.)3307.0NaNUndefinedNaN1572.0NaN0000000
3794housesushant lok 1 builder floorssector 436.2530728.02034.0Plot area 226(188.96 sq.m.)773+3.0NorthModerately OldNaN226.0NaN11100046
3796flatbptp terrasector 37d1.757987.02191.0Super Built up area 2191(203.55 sq.m.)Built Up area: 2100 sq.ft. (195.1 sq.m.)Carpet area: 1800 sq.ft. (167.23 sq.m.)4437.0WestRelatively New2191.02100.01800.00000012120
3797flatgreenopolissector 890.904700.01915.0Built Up area: 1910 (177.44 sq.m.)33014.0NaNUnder ConstructionNaN1910.0NaN00000067
3798flatmapsko mount villesector 791.558539.01815.0Super Built up area 1815(168.62 sq.m.)Carpet area: 1071.33 sq.ft. (99.53 sq.m.)34311.0South-WestRelatively New1815.0NaN1071.33010002152
3799flatemaar palm gardenssector 831.729052.01900.0Super Built up area 1900(176.52 sq.m.)Built Up area: 1600 sq.ft. (148.64 sq.m.)Carpet area: 1240.04 sq.ft. (115.2 sq.m.)3334.0SouthRelatively New1900.01600.01240.04010002165
3800houseindependentsector 4012.0038986.03078.0Plot area 342(285.96 sq.m.)16163+4.0NaNNew PropertyNaN342.0NaN1111020
3801flatbaani city centersector 630.8810945.0804.0Built Up area: 804 (74.69 sq.m.)Carpet area: 600 sq.ft. (55.74 sq.m.)1124.0NaNUndefinedNaN804.0600.000000000
3802flatorchid petalssector 494.7911628.04119.0Super Built up area 4115(382.3 sq.m.)553+14.0SouthRelatively New4115.0NaNNaN11010049